OpenRaiser/Intern-Atlas huggingface dataset:
A Journey Among Java Neutral Program Variants
Venue: arXiv cs.SE 2019
Year: 2019

Neutral program variants are functionally similar to an original program, yet implement slightly different behaviors. Techniques such as approximate computing or genetic improvement share the intuition that potential for enhancements lies in these acceptable behavioral differences (e.g., enhanced performance or reliability). Yet, the automatic synthesis of neutral program variants, through speculative transformations remains a key challenge. This work aims at characterizing plastic code regions in Java programs, i.e., the areas that are prone to the synthesis of neutral program variants. Our empirical study relies on automatic variations of 6 real-world Java programs. First, we transform these programs with three state-of-the-art speculative transformations: add, replace and delete statements. We get a pool of 23445 neutral variants, from which we gather the following novel insights: developers naturally write code that supports fine-grain behavioral changes; statement deletion is a surprisingly effective speculative transformation; high-level design decisions, such as the choice of a data structure, are natural points that can evolve while keeping functionality. Second, we design 3 novel speculative transformations, targeted at specific plastic regions. New experiments reveal that respectively 60\%, 58\% and 73\% of the synthesized variants (175688 in total) are neutral and exhibit execution traces that are different from the original.
Collaborative Execution of Deep Neural Networks on Internet of Things Devices
Venue: arXiv cs.CV 2019
Year: 2019

With recent advancements in deep neural networks (DNNs), we are able to solve traditionally challenging problems. Since DNNs are compute intensive, consumers, to deploy a service, need to rely on expensive and scarce compute resources in the cloud. This approach, in addition to its dependability on high-quality network infrastructure and data centers, raises new privacy concerns. These challenges may limit DNN-based applications, so many researchers have tried optimize DNNs for local and in-edge execution. However, inadequate power and computing resources of edge devices along with small number of requests limits current optimizations applicability, such as batch processing. In this paper, we propose an approach that utilizes aggregated existing computing power of Internet of Things (IoT) devices surrounding an environment by creating a collaborative network. In this approach, IoT devices cooperate to conduct single-batch inferencing in real time. While exploiting several new model-parallelism methods and their distribution characteristics, our approach enhances the collaborative network by creating a balanced and distributed processing pipeline. We have illustrated our work using many Raspberry Pis with studying DNN models such as AlexNet, VGG16, Xception, and C3D.
Coercion-Resistant Voting in Linear Time via Fully Homomorphic Encryption: Towards a Quantum-Safe Scheme
Venue: arXiv cs.CR 2019
Year: 2019

We present an approach for performing the tallying work in the coercion-resistant JCJ voting protocol, introduced by Juels, Catalano, and Jakobsson, in linear time using fully homomorphic encryption (FHE). The suggested enhancement also paves the path towards making JCJ quantum-resistant, while leaving the underlying structure of JCJ intact. The exhaustive, comparison-based approach of JCJ using plaintext equivalence tests leads to a quadratic blow-up in the number of votes, which makes the tallying process rather impractical in realistic settings with a large number of voters. We show how the removal of invalid votes can be done in linear time via a solution based on recent advances in various FHE primitives such as hashing, zero-knowledge proofs of correct decryption, verifiable shuffles and threshold FHE. We conclude by touching upon some of the advantages and challenges of such an approach, followed by a discussion of further security and post-quantum considerations.
MOANA: An Online Learned Adaptive Appearance Model for Robust Multiple Object Tracking in 3D
Venue: arXiv cs.CV 2019
Year: 2019

Multiple object tracking has been a challenging field, mainly due to noisy detection sets and identity switch caused by occlusion and similar appearance among nearby targets. Previous works rely on appearance models built on individual or several selected frames for the comparison of features, but they cannot encode long-term appearance changes caused by pose, viewing angle and lighting conditions. In this work, we propose an adaptive model that learns online a relatively long-term appearance change of each target. The proposed model is compatible with any feature of fixed dimension or their combination, whose learning rates are dynamically controlled by adaptive update and spatial weighting schemes. To handle occlusion and nearby objects sharing similar appearance, we also design cross-matching and re-identification schemes based on the application of the proposed adaptive appearance models. Additionally, the 3D geometry information is effectively incorporated in our formulation for data association. The proposed method outperforms all the state-of-the-art on the MOTChallenge 3D benchmark and achieves real-time computation with only a standard desktop CPU. It has also shown superior performance over the state-of-the-art on the 2D benchmark of MOTChallenge.
Interim Report on Adaptive Event Dispatching in Serverless Computing Infrastructures
Venue: arXiv cs.DC 2019
Year: 2019

Serverless computing is an emerging service model in distributed computing systems. The term captures cloud-based event-driven distributed application design and stems from its completely resource-transparent deployment model, i.e. serverless. This work thesisizes that adaptive event dispatching can improve current serverless platform resource efficiency by considering locality and dependencies. These design contemplations have also been formulated by Hendrickson et al., which identifies the requirement that "Serverless load balancers must make low-latency decisions while considering session, code and data locality". This interim report investigates the economical importance of the emerging trend and asserts that existing serverless platforms still do not optimize for data locality, whereas a variety of scheduling methods are available from distributed computing research which have proven to increase resource efficiency.
Lambda Calculus and Probabilistic Computation
Venue: arXiv cs.LO 2019
Year: 2019

We introduce two extensions of the $λ$-calculus with a probabilistic choice operator, $Λ_\oplus^{cbv}$ and $Λ_\oplus^{cbn}$, modeling respectively call-by-value and call-by-name probabilistic computation. We prove that both enjoys confluence and standardization, in an extended way: we revisit these two fundamental notions to take into account the asymptotic behaviour of terms. The common root of the two calculi is a further calculus based on Linear Logic, $Λ_\oplus^!$, which allows for a fine control of the interaction between choice and copying, and which allows us to develop a unified, modular approach.
SalSi: A new seismic attribute for salt dome detection
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human vision system (HVS). In this work, we aim to highlight the parts of the seismic volume that receive highest attention from the human interpreter, and based on the salient features of a seismic image, we detect the salt domes. Experimental results show the effectiveness of SalSi on the real seismic dataset acquired from the North Sea, F3 block. Subjectively, we have used the ground truth and the output of different salt dome delineation algorithms to validate the results of SalSi. For the objective evaluation of results, we have used the receiver operating characteristics (ROC) curves and area under the curves (AUC) to demonstrate SalSi is a promising and an effective attribute for seismic interpretation.
Modeling Grasp Type Improves Learning-Based Grasp Planning
Venue: arXiv cs.RO 2019
Year: 2019

Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while holding a pen to write requires a multi-fingered precision grasp to impart dexterity on the object. In this paper, we propose a probabilistic grasp planner that explicitly models grasp type for planning high-quality precision and power grasps in real-time. We take a learning approach in order to plan grasps of different types for previously unseen objects when only partial visual information is available. Our work demonstrates the first supervised learning approach to grasp planning that can explicitly plan both power and precision grasps for a given object. Additionally, we compare our learned grasp model with a model that does not encode type and show that modeling grasp type improves the success rate of generated grasps. Furthermore we show the benefit of learning a prior over grasp configurations to improve grasp inference with a learned classifier.
Secure and Computationally-Efficient Cryptographic Primitive based on Cellular Automation
Venue: arXiv cs.CR 2019
Year: 2019

Mageto, a random number generator based on one-dimensional cellular automaton (CA) is presented. Three procedures of secure implementation using Mageto is proposed and discussed. Implementations are very efficient in a wide range of hardware and software scenarios. It includes the advanced application of the Internet of Things (IoT) and cyber-physical systems which are both needed for computationally-efficient cryptographic primitives. Furthermore, the proposed primitive is inherently resistant against the Side Channel Attack (SCA), where many currently available ciphers, such as AES, require additional hardware or software effort to prevent SCA line of attack.
Sentence Rewriting for Semantic Parsing
Venue: arXiv cs.CL 2019
Year: 2019

A major challenge of semantic parsing is the vocabulary mismatch problem between natural language and target ontology. In this paper, we propose a sentence rewriting based semantic parsing method, which can effectively resolve the mismatch problem by rewriting a sentence into a new form which has the same structure with its target logical form. Specifically, we propose two sentence-rewriting methods for two common types of mismatch: a dictionary-based method for 1-N mismatch and a template-based method for N-1 mismatch. We evaluate our entence rewriting based semantic parser on the benchmark semantic parsing dataset -- WEBQUESTIONS. Experimental results show that our system outperforms the base system with a 3.4% gain in F1, and generates logical forms more accurately and parses sentences more robustly.
A Multi-Object Rectified Attention Network for Scene Text Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Irregular text is widely used. However, it is considerably difficult to recognize because of its various shapes and distorted patterns. In this paper, we thus propose a multi-object rectified attention network (MORAN) for general scene text recognition. The MORAN consists of a multi-object rectification network and an attention-based sequence recognition network. The multi-object rectification network is designed for rectifying images that contain irregular text. It decreases the difficulty of recognition and enables the attention-based sequence recognition network to more easily read irregular text. It is trained in a weak supervision way, thus requiring only images and corresponding text labels. The attention-based sequence recognition network focuses on target characters and sequentially outputs the predictions. Moreover, to improve the sensitivity of the attention-based sequence recognition network, a fractional pickup method is proposed for an attention-based decoder in the training phase. With the rectification mechanism, the MORAN can read both regular and irregular scene text. Extensive experiments on various benchmarks are conducted, which show that the MORAN achieves state-of-the-art performance. The source code is available.
PVSS: A Progressive Vehicle Search System for Video Surveillance Networks
Venue: arXiv cs.CV 2019
Year: 2019

This paper is focused on the task of searching for a specific vehicle that appeared in the surveillance networks. Existing methods usually assume the vehicle images are well cropped from the surveillance videos, then use visual attributes, like colors and types, or license plate numbers to match the target vehicle in the image set. However, a complete vehicle search system should consider the problems of vehicle detection, representation, indexing, storage, matching, and so on. Besides, attribute-based search cannot accurately find the same vehicle due to intra-instance changes in different cameras and the extremely uncertain environment. Moreover, the license plates may be misrecognized in surveillance scenes due to the low resolution and noise. In this paper, a Progressive Vehicle Search System, named as PVSS, is designed to solve the above problems. PVSS is constituted of three modules: the crawler, the indexer, and the searcher. The vehicle crawler aims to detect and track vehicles in surveillance videos and transfer the captured vehicle images, metadata and contextual information to the server or cloud. Then multi-grained attributes, such as the visual features and license plate fingerprints, are extracted and indexed by the vehicle indexer. At last, a query triplet with an input vehicle image, the time range, and the spatial scope is taken as the input by the vehicle searcher. The target vehicle will be searched in the database by a progressive process. Extensive experiments on the public dataset from a real surveillance network validate the effectiveness of the PVSS.
New Radon Transform Based Texture Features of Handwritten Document
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present some new features describing the handwritten document as a texture. These features are based on the Radon transform. All values can be obtained easily and suit for the coarse classification of documents.
Adaptive Event Dispatching in Serverless Computing Infrastructures
Venue: arXiv cs.DC 2019
Year: 2019

Serverless computing is an emerging Cloud service model. It is currently gaining momentum as the next step in the evolution of hosted computing from capacitated machine virtualisation and microservices towards utility computing. The term "serverless" has become a synonym for the entirely resource-transparent deployment model of cloud-based event-driven distributed applications. This work investigates how adaptive event dispatching can improve serverless platform resource efficiency and contributes a novel approach that allows for better scaling and fitting of the platform's resource consumption to actual demand.
Automating the search for a patent's prior art with a full text similarity search
Venue: arXiv cs.IR 2019
Year: 2019

More than ever, technical inventions are the symbol of our society's advance. Patents guarantee their creators protection against infringement. For an invention being patentable, its novelty and inventiveness have to be assessed. Therefore, a search for published work that describes similar inventions to a given patent application needs to be performed. Currently, this so-called search for prior art is executed with semi-automatically composed keyword queries, which is not only time consuming, but also prone to errors. In particular, errors may systematically arise by the fact that different keywords for the same technical concepts may exist across disciplines. In this paper, a novel approach is proposed, where the full text of a given patent application is compared to existing patents using machine learning and natural language processing techniques to automatically detect inventions that are similar to the one described in the submitted document. Various state-of-the-art approaches for feature extraction and document comparison are evaluated. In addition to that, the quality of the current search process is assessed based on ratings of a domain expert. The evaluation results show that our automated approach, besides accelerating the search process, also improves the search results for prior art with respect to their quality.
Emotion Detection using Data Driven Models
Venue: arXiv cs.CL 2019
Year: 2019

Text is the major method that is used for communication now a days, each and every day lots of text are created. In this paper the text data is used for the classification of the emotions. Emotions are the way of expression of the persons feelings which has an high influence on the decision making tasks. Datasets are collected which are available publically and combined together based on the three emotions that are considered here positive, negative and neutral. In this paper we have proposed the text representation method TFIDF and keras embedding and then given to the classical machine learning algorithms of which Logistics Regression gives the highest accuracy of about 75.6%, after which it is passed to the deep learning algorithm which is the CNN which gives the state of art accuracy of about 45.25%. For the research purpose the datasets that has been collected are released.
An Improved Cooperative Repair Scheme for Reed-Solomon Codes
Venue: arXiv cs.IT 2019
Year: 2019

Dau et al. recently extend Guruswami and Wootters' scheme (STOC'2016) to cooperatively repair two or three erasures in Reed-Solomon (RS) codes. However, their scheme restricts to either the case that the characteristic of $F$ divides the extension degree $[F\!:\!B]$ or some special failure patterns, where $F$ is the base field of the RS code and $B$ is the subfield of the repair symbols. In this paper, we derive an improved cooperative repair scheme that removes all these restrictions. That is, our scheme applies to any characteristic of $F$ and can repair all failure patterns of two or three erasures.
The Complete Hierarchical Locality of the Punctured Simplex Code
Venue: arXiv cs.IT 2019
Year: 2019

This paper presents a new alphabet-dependent bound for codes with hierarchical locality. Then, the complete list of possible localities is derived for a class of codes obtained by deleting specific columns from a Simplex code. This list is used to show that these codes are optimal codes with hierarchical locality.
Motion Perception in Reinforcement Learning with Dynamic Objects
Venue: arXiv cs.LG 2019
Year: 2019

In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken. However, in existing reinforcement learning works motion is rarely treated explicitly; it is rather assumed that the controller learns the necessary motion representation from temporal stacks of frames implicitly. In this paper, we show that for continuous control tasks learning an explicit representation of motion improves the quality of the learned controller in dynamic scenarios. We demonstrate this on common benchmark tasks (Walker, Swimmer, Hopper), on target reaching and ball catching tasks with simulated robotic arms, and on a dynamic single ball juggling task. Moreover, we find that when equipped with an appropriate network architecture, the agent can, on some tasks, learn motion features also with pure reinforcement learning, without additional supervision. Further we find that using an image difference between the current and the previous frame as an additional input leads to better results than a temporal stack of frames.
Dynamic EEE Coalescing: Techniques and Bounds
Venue: arXiv cs.NI 2019
Year: 2019

Frame coalescing is one of the most efficient techniques to manage the low power idle (LPI) mode supported by Energy Efficient Ethernet (EEE) interfaces. This technique enables EEE interfaces to remain in the LPI mode for a certain amount of time upon the arrival of the first frame (time-based coalescing) or until a predefined amount of traffic accumulates in the transmission buffer (size-based coalescing). This paper provides new insights on the practical efficiency limits of both coalescing techniques. In particular, we derive the fundamental limits on the maximum energy savings considering a target average frame delay. Additionally, we present new open-loop adaptive variants of both time-based and size-based coalescing techniques. These proposals dynamically adjust the length of the sleeping periods in accordance with actual traffic conditions to reduce energy consumption while keeping the average delay near a predefined value simultaneously. Analytical and simulation results show that the energy consumption of both proposals is comparable to the fundamental limits. Consequently, we recommend the usage of the time-based algorithm in most scenarios because of its simplicity as well as its ability to bound the maximum frame delay at the same time.
On Secure Capacity of Multiple Unicast Traffic over Separable Networks
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies the problem of information theoretic secure communication when a source has private messages to transmit to $m$ destinations, in the presence of a passive adversary who eavesdrops an unknown set of $k$ edges. The information theoretic secure capacity is derived over unit-edge capacity separable networks, for the cases when $k=1$ and $m$ is arbitrary, or $m=3$ and $k$ is arbitrary. This is achieved by first showing that there exists a secure polynomial-time code construction that matches an outer bound over two-layer networks, followed by a deterministic mapping between two-layer and arbitrary separable networks.
A Reduced-Complexity Projection Algorithm for ADMM-based LP Decoding
Venue: arXiv cs.IT 2019
Year: 2019

The Alternating Direction Method of Multipliers has recently been adapted for Linear Programming Decoding of Low-Density Parity-Check codes. The computation of the projection onto the parity polytope is the core of this algorithm and usually involves a sorting operation, which is the main effort of the projection. In this paper, we present an algorithm with low complexity to compute this projection. The algorithm relies on new findings in the recursive structure of the parity polytope and iteratively fixes selected components. It requires up to 37% less arithmetical operations compared to state-of-the-art projections. Additionally, it does not involve a sorting operation, which is needed in all exact state-of-the-art projection algorithms. These two benefits make it appealing for efficient hard- and software implementations.
INTERPOL: Information Theoretically Verifiable Polynomial Evaluation
Venue: arXiv cs.CR 2019
Year: 2019

We study the problem of verifiable polynomial evaluation in the user-server and multi-party setups. We propose {INTERPOL}, an information-theoretically verifiable algorithm that allows a user to delegate the evaluation of a polynomial to a server, and verify the correctness of the results with high probability and in sublinear complexity. Compared to the existing approaches which typically rely on cryptographic assumptions, {INTERPOL} stands out in that it does not assume any computational limitation on the server. {INTERPOL} relies on decomposition of polynomial evaluation into two matrix multiplications, and injection of computation redundancy in the form of locally computed parities with secret coefficients for verification. We show that {INTERPOL} has several desirable properties such as adaptivity and public verifiability. Furthermore, by generalizing {INTERPOL} to a multi-party setting consisting of a network of $n$ untrusted nodes, where each node is interested in evaluating the same polynomial, we demonstrate that we can achieve an overall computational complexity comparable to a trusted setup, while guaranteeing information-theoretic verification at each node.